论文标题
Shot-Vae：具有标签吸引Elbo近似的半监督深度生成模型
SHOT-VAE: Semi-supervised Deep Generative Models With Label-aware ELBO Approximations
论文作者
论文摘要
半监督的变异自动编码器（VAE）取得了强烈的结果，但也遇到了挑战，即良好的Elbo值并不总是暗示准确的推理结果。在本文中，我们调查并提出了两个问题的原因：（1）Elbo目标无法直接利用标签信息。 （2）存在瓶颈价值，并在此值之后继续优化Elbo不会提高推理准确性。在实验结果的基础上，我们提出shot-vae来解决这些问题，而无需引入额外的先验知识。 Shot-Vae提供了两个贡献：（1）一种名为Smooth-Elbo的新ELBO近似，将标签预测性损失整合到Elbo中。 （2）基于最佳插值的近似值，通过降低Elbo和数据可能性之间的边缘来打破Elbo值瓶颈。 Shot-Vae以10K标签的CIFAR-100上的错误率为25.30％的误差率达到了良好的性能，并使用4K标签将错误率降低到CIFAR-10的6.11％。
Semi-supervised variational autoencoders (VAEs) have obtained strong results, but have also encountered the challenge that good ELBO values do not always imply accurate inference results. In this paper, we investigate and propose two causes of this problem: (1) The ELBO objective cannot utilize the label information directly. (2) A bottleneck value exists and continuing to optimize ELBO after this value will not improve inference accuracy. On the basis of the experiment results, we propose SHOT-VAE to address these problems without introducing additional prior knowledge. The SHOT-VAE offers two contributions: (1) A new ELBO approximation named smooth-ELBO that integrates the label predictive loss into ELBO. (2) An approximation based on optimal interpolation that breaks the ELBO value bottleneck by reducing the margin between ELBO and the data likelihood. The SHOT-VAE achieves good performance with a 25.30% error rate on CIFAR-100 with 10k labels and reduces the error rate to 6.11% on CIFAR-10 with 4k labels.
